Predicting User’s Multi-Interests With Network Embedding in Health-Related Topics
Zhipeng Jin1; Ruoran Liu1; Qiudan Li1; Daniel D. Zeng1; YongCheng Zhan2; Lei Wang1,2
2016
会议名称2016 International Joint Conference on Neural Networks
会议录名称IJCNN2016
会议日期24-29 July 2016
会议地点Vancouver, Canada
其他摘要With the rapid growth of Web 2.0, social media has become a prevalent information sharing and seeking channel for health surveillance, in which users form interactive networks by posting and replying messages, providing and rating reviews, attending multiple discussion boards on health-related topics. Users’ behaviors in these interactive networks reflect users’ multiple interests. To provide better information service for users, it is necessary to analyze the user interactions and predict users’ multi-interests. Most existing work in predicting users’ multi-interests based on multi label network classification focuses on using approximate inference methods to leverage the dependency information to improve classification results. Inspired by deep learning techniques, DEEPWARK learns label independent latent representations of vertices in a network using local information obtained from truncated random walks, which provides an efficient way for predicting users multi-interests from user interactions.  In this paper, we develop a user’s multi-interests prediction model based on DEEPWALK, weight information of user interactions is considered when modeling a stream of short constrained random walks and SkipGram is employed to generate more accurate representations of user vertices, which help identify users’ interests. Experimental results on two real world health-related datasets show the efficacy of the proposed model.
关键词User Interaction Network Multi-interest Prediction Weight Information
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12273
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
通讯作者Qiudan Li
作者单位1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing 100190, China
2.Department of Management Information Systems University of Arizona Tucson, Arizona, USA
推荐引用方式
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Zhipeng Jin,Ruoran Liu,Qiudan Li,等. Predicting User’s Multi-Interests With Network Embedding in Health-Related Topics[C],2016.
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